CN114771502A - Energy consumption optimization method, device, equipment, storage medium and system - Google Patents
Energy consumption optimization method, device, equipment, storage medium and system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
- B60W40/09—Driving style or behaviour
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0029—Mathematical model of the driver
Abstract
The application discloses a vehicle energy consumption optimization method, a device, equipment, a storage medium and a system, wherein the method comprises the following steps: generating a personnel tag based on personal information of a driver and historical driving data thereof; analyzing the driving behavior of the driver based on the personnel label and the current driving data to obtain the type of the current driving behavior of the driver; acquiring target driving data in a preset time period before and after the occurrence of the high energy consumption event, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data monitored by the vehicle-mounted monitoring terminal in the current driving data; and based on the type and the correlation, proposing energy consumption optimization suggestions to the driver and the vehicle. According to the energy consumption optimization method and device, the energy consumption correlation factors can be more comprehensively mined, and the proposed energy consumption optimization suggestions are more accurate and comprehensive.
Description
Technical Field
The invention relates to the technical field of energy-saving driving, in particular to a vehicle energy consumption optimization method, device, equipment, storage medium and system.
Background
How to reduce the TCO (total cost of ownership) of operating vehicles is the subject of intensive research of vehicle manufacturers, wherein vehicle energy consumption is one of the key influencing factors in the TCO, and therefore, the TCO of operating vehicles can be reduced through energy consumption optimization.
In the prior art, energy consumption optimization of a traditional vehicle is to evaluate driving behaviors of a driver based on the driving behaviors of the driver and state data of the vehicle, so that energy consumption optimization suggestions are provided; the energy consumption analysis method ignores personal factors of drivers, so that the method is not comprehensive in mining energy consumption related factors, and the proposed energy consumption optimization suggestion is inaccurate and incomplete.
Therefore, there is a technical problem in the prior art that the proposed energy consumption optimization proposal is inaccurate and incomplete.
Disclosure of Invention
The invention mainly aims to provide a vehicle energy consumption optimization method, a vehicle energy consumption optimization device, vehicle energy consumption optimization equipment, a vehicle energy consumption optimization storage medium and a vehicle energy consumption optimization system, and aims to solve the technical problem that the proposed energy consumption optimization suggestion is inaccurate and incomplete;
in order to achieve the above object, the present invention provides a vehicle energy consumption optimization method, including the steps of:
generating a personnel tag based on personal information of a driver and historical driving data thereof;
evaluating the driving behavior of the driver based on the personnel label and the current driving data to obtain an evaluation result;
determining a correlation of a high energy consumption event with travel data within the high energy consumption event occurrence period;
and based on the evaluation result and the correlation, proposing energy consumption optimization suggestions to the driver and the vehicle.
In a possible embodiment of the present application, the step of evaluating the driving behavior of the driver based on the staff tags and the current driving data to obtain an evaluation result includes:
generating a personnel tag based on personal information of a driver and historical driving data thereof;
analyzing the driving behavior of the driver based on the personnel label and the current driving data to obtain the type of the current driving behavior of the driver;
acquiring target driving data in a preset time period before and after the high energy consumption event occurs, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on monitoring energy consumption data in the current driving data by a vehicle-mounted monitoring terminal;
and based on the type and the correlation, proposing energy consumption optimization suggestions to the driver and the vehicle.
In a possible embodiment of the present application, the step of analyzing the driving behavior of the driver based on the staff tags and the current driving data to obtain a type of the current driving behavior of the driver includes:
inputting the current driving data into a preset driving behavior analysis model;
and analyzing and processing the current driving data based on the preset driving behavior analysis model to obtain the type of the driving behavior of the driver, wherein the preset driving behavior analysis model is obtained by iteratively training a preset model to be trained based on training data with type labels.
In a possible implementation manner of the present application, the step of analyzing and processing the current driving data based on the preset driving behavior analysis model to obtain the type of the driving behavior of the driver includes:
calculating the current driving data to obtain at least one current driving behavior index;
determining a target score of each current driving behavior index based on the corresponding relation between the preset driving behavior index and the target score;
calculating the target score based on an entropy weight method to obtain the weight of each driving behavior index;
calculating the target score of each current driving behavior index based on the weight of each driving behavior index to obtain a first calculation result;
determining an adjustment coefficient for the first calculation result based on a preset personnel tag and the current driving data;
adjusting the first calculation result based on the adjustment coefficient to obtain a second calculation result;
and determining the type of the current driving behavior of the driver based on the corresponding relation between the preset second calculation result and the driving behavior type.
In a possible embodiment of the application, the step of calculating the target score based on an entropy weight method to obtain the weight of each driving behavior index further includes:
carrying out standardization processing on the target score to obtain a standardized target score;
calculating the standardized target score to obtain the information entropy of each current driving behavior index;
and obtaining the weight corresponding to each current driving behavior index based on the information entropy and the target score.
In one possible embodiment of the present application, the step of determining the correlation between the high energy consumption event and the target driving data includes:
and analyzing the variation trend of each driving data in the high-energy-consumption event occurrence period on the same time axis to obtain the correlation between the high-energy-consumption event and the target driving data.
In one possible embodiment of the present application, after the step of determining the correlation of the high energy consumption event with the target driving data, the method further comprises:
acquiring original driving data of a current vehicle, and arranging the original driving data according to a time sequence of occurrence to obtain a time sequence database;
and performing frequency analysis on the time sequence data related to the high energy consumption events in the time sequence database, determining the data characteristics of the time sequence data related to the high energy consumption events, and generating an energy consumption experience database for use in vehicle research and development.
The present application further provides a vehicle energy consumption optimization device, the device includes:
the generation module is used for generating a personnel label based on the personal information of the driver and the historical driving data thereof;
the first determining module is used for analyzing the driving behavior of the driver based on the personnel tag and the current driving data to obtain the type of the current driving behavior of the driver;
the second determining module is used for acquiring target driving data in a preset time period before and after a high energy consumption event occurs and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on monitoring energy consumption data in the current driving data by the vehicle-mounted monitoring terminal;
and the proposing module is used for proposing energy consumption optimization suggestions to the driver and the vehicle based on the type and the correlation.
The present application further provides a vehicle energy consumption optimizing apparatus, the apparatus comprising: a memory, a processor, and a vehicle energy consumption optimization program stored on the memory and executable on the processor, the vehicle energy consumption optimization program configured to implement the steps of the vehicle energy consumption optimization method of any of the above.
The present application further provides a vehicle energy consumption optimization system, the system comprising: vehicle-mounted monitoring terminal and any one of the above vehicle energy consumption optimizing device.
Compared with the energy consumption analysis method in the prior art, which evaluates the driving behavior of a driver based on the driving data of the driver so as to provide energy consumption optimization suggestions, the energy consumption optimization method generates personnel labels based on the personal information and the historical driving data of the driver; analyzing the driving behavior of the driver based on the personnel label and the current driving data to obtain the type of the current driving behavior of the driver; acquiring target driving data in a preset time period before and after the high energy consumption event occurs, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on monitoring energy consumption data in the current driving data by a vehicle-mounted monitoring terminal; and based on the type and the correlation, proposing energy consumption optimization suggestions to the driver and the vehicle. It can be understood that the driving behavior of the driver is analyzed based on the current driving data of the driver and the personnel label of the driver, correlation analysis is performed on the driving data in the preset time period before and after the occurrence of the high energy consumption event, and based on the type and the correlation, energy consumption optimization suggestions are provided for the driver and the vehicle, so that energy consumption correlation factors can be more comprehensively mined, and the provided energy consumption optimization suggestions are more accurate and comprehensive.
Drawings
FIG. 1 is a schematic flow chart diagram illustrating a first embodiment of a vehicle energy consumption optimization method of the present application;
FIG. 2 is a schematic diagram of a first scenario involved in the vehicle energy consumption optimization method of the present application;
FIG. 3 is a schematic structural diagram of a vehicle energy consumption optimization device for a hardware operating environment according to an embodiment of the present invention;
the implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The embodiment of the application provides a vehicle energy consumption optimization method, and referring to fig. 1, fig. 1 is a schematic flow diagram of an embodiment of the vehicle energy consumption optimization method.
In this embodiment, the vehicle energy consumption optimization method includes:
step S10: generating a personnel tag based on personal information of a driver and historical driving data thereof;
step S20: analyzing the driving behavior of the driver based on the personnel label and the current driving data to obtain the type of the current driving behavior of the driver;
step S30: acquiring target driving data in a preset time period before and after the occurrence of the high energy consumption event, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on the energy consumption data monitored by the vehicle-mounted monitoring terminal in the current driving data;
step S40: and based on the type and the correlation, proposing energy consumption optimization suggestions to the driver and the vehicle.
The present embodiment aims to solve the following problems: the proposed energy consumption optimization suggests inaccurate and incomplete technical problems. Compared with the prior art that the driving data of the driver are based on, the driving behavior of the driver is evaluated, and the energy consumption optimization suggestion is provided, the driving behavior of the driver is evaluated based on the driving data of the driver and the personnel label of the driver, the energy consumption optimization suggestion is provided for the driver and the vehicle by combining the correlation between the high energy consumption event and the driving data in the preset time period before and after the high energy consumption event occurs, and the accurate and comprehensive energy consumption optimization suggestion can be provided.
As an example, the vehicle energy consumption optimization method may be applied to a vehicle energy consumption optimization device, which belongs to a vehicle energy consumption optimization system.
As an example, the vehicle includes a commercial vehicle and a private vehicle, wherein the commercial vehicle may be a truck, a passenger car with nine seats or more, and the like, and is not limited in particular.
For convenience of description, the following description will be given by taking a vehicle as a commercial vehicle.
As an example, the vehicle energy consumption is energy consumption during vehicle driving, and may be vehicle oil consumption, alcohol consumption, and the like, and is not limited specifically.
As an example, the vehicle energy consumption optimization may be optimization of energy consumption by a driver during driving, optimization of energy consumption by improving quality of a vehicle during vehicle development, and the like, and is not limited in particular.
As one example, the drivers of the vehicles are collectively managed by a vehicle-related operating company, which manages personal information of the drivers as well as historical driving information.
As an example, the personal information of the driver includes, without limitation, the age, sex, height, weight, and the like of the driver.
As an example, the historical driving data includes, but is not limited to, a historical driving trace, a historical vehicle state, and the like.
As an example, referring to fig. 2, when a driver uses a vehicle, the driver needs to input information such as age, height, and weight of the person through a user terminal, and upload a picture of the person through a user terminal, and the user terminal uploads the information input by the driver and the uploaded picture to an intelligent internet platform and uploads the information and the picture to a big data center through the intelligent internet platform.
As an example, referring to fig. 2, when a driver starts a vehicle, a face camera of the in-vehicle driving recorder collects a face image of the driver and uploads the face image to a video or picture server, and the video or picture server extracts information such as gender, age, and the like based on the face image, uploads the extracted information to the intelligent internet platform, and uploads the extracted information to a big data center through the intelligent internet platform.
As an example, the driver may upload personal information, photos, and the like through the mobile terminal and the in-vehicle infotainment device, which is not limited in particular.
As an example, the intelligent networking platform checks the information input by the user terminal and the uploaded photos based on the information extracted by the video or picture server, so as to ensure the validity of the information, and archives the information.
As an example, when a driver uses a Vehicle, the driver needs to input Vehicle basic information such as a VIN (Vehicle Identification Number) of the currently running Vehicle through a user terminal, and the intelligent networking platform binds and archives the driver and the Vehicle so as to call historical running data of the driver next time.
As an example, referring to fig. 2, in a vehicle driving process, an ecu (electronic Control unit) electronic Control unit (also called a vehicle-mounted computer) in a vehicle-mounted monitoring terminal periodically broadcasts data of a vehicle driving state, a vehicle speed, an accelerator opening, a gear position, a brake opening, and the like to a CAN (Controller Area Network) bus, and uploads the data to a big data center through a Network module, where the Network module may be a 4G Network module or a 5G Network module, and the like, and is not limited specifically.
The method comprises the following specific steps of,
step S10: generating a personnel tag based on personal information of a driver and historical driving data thereof;
in the embodiment, the big data center acquires the personal information and the historical driving data of the driver, and can acquire the personal information and the historical driving behavior of the driver and generate the personnel label based on the personal information and the historical driving data of the driver; the personnel labels comprise a gender label, an age label, a driving habit label, a travel habit label and the like.
Step S20: acquiring target driving data in a preset time period before and after the high energy consumption event occurs, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on monitoring energy consumption data in the current driving data by a vehicle-mounted monitoring terminal;
as an example, the current driving data is all driving data recorded while the driver drives the current vehicle.
As one example, the current running data includes vehicle running state data, vehicle positioning data, vehicle actual load, vehicle external condition data, and the like.
In the present embodiment, the driving behavior types are classified into three types: the safety type, the economy type and the loss type can classify different driving behaviors of different drivers in the driving process based on the current driving data and the personnel labels, and provide personalized energy consumption optimization suggestions for the drivers based on classification results.
In this embodiment, the step of analyzing the driving behavior of the driver based on the staff tags and the current driving data to obtain the type of the current driving behavior of the driver includes:
step A1: inputting the current driving data into a preset driving behavior analysis model;
step A2: and analyzing and processing the current driving data based on the preset driving behavior analysis model to obtain the type of the driving behavior of the driver, wherein the preset driving behavior analysis model is obtained by iteratively training a preset model to be trained based on training data with type labels.
In this embodiment, the current driving data is input to a preset driving behavior analysis model, and the current driving data is analyzed and processed based on the preset driving behavior analysis model to obtain the type of the driving behavior of the driver, where the preset driving behavior analysis model is obtained by performing iterative training on a preset model to be trained based on training data with type labels.
As an example, historical driving data is obtained, a type label of the historical driving data is generated, and a preset model to be trained is iteratively trained based on the historical driving data and the type label of the historical driving data, so as to obtain the driving behavior analysis model meeting a precision condition.
The step of performing iterative training on a preset model to be trained based on the historical driving data and the type label of the historical driving data to obtain the driving behavior analysis model meeting the precision condition includes:
inputting the historical driving data into the preset model to be trained to obtain the type of the historical driving behavior; performing difference calculation on the historical driving data and the type labels of the historical driving data to obtain an error result; and judging whether the error result meets the error standard indicated by a preset error threshold range or not based on the error result.
And if the error result does not meet the error standard indicated by the preset error threshold range, returning to the step of inputting the current driving data into a preset driving behavior analysis model, and analyzing and processing the current driving data based on the preset driving behavior analysis model until the training error result meets the error standard indicated by the preset error threshold range, and stopping training to obtain the driving behavior analysis model.
In this embodiment, the step of analyzing and processing the current driving data based on the preset driving behavior analysis model to obtain the type of the driving behavior of the driver includes:
step B1: calculating the current driving data to obtain at least one current driving behavior index;
step B2: determining a target score of each current driving behavior index based on the corresponding relation between the preset driving behavior index and the target score;
step B3: calculating the target score based on an entropy weight method to obtain the weight of each driving behavior index;
in this embodiment, current driving data is obtained, and the current driving data is calculated to obtain at least one current driving behavior index, where the driving behavior index may be percentage of idle time to total driving time, average energy consumption, percentage of overspeed driving time to total driving time, braking frequency, and the like, and is not limited specifically.
In this embodiment, the target score of each current driving behavior index is determined based on a corresponding relationship between a preset driving behavior index and a target score, where the corresponding relationship between the driving behavior index and the target score is determined based on a numerical value of the driving behavior index and historical empirical effect data.
As an example, different values of the idle time period as a percentage of the total travel time period correspond to different target points.
In this embodiment, the target score is calculated based on an entropy weight method, so as to obtain the weight of each driving behavior index.
Step B4: calculating the target score of each current driving behavior index based on the weight of each driving behavior index to obtain a first calculation result;
step B5: determining an adjustment coefficient for the first calculation result based on a preset personnel tag and the current driving data;
step B6: adjusting the first calculation result based on the adjustment coefficient to obtain a second calculation result;
step B7: and determining the type of the current driving behavior of the driver based on the corresponding relation between the preset second calculation result and the driving behavior type.
In this embodiment, the target score of each current driving behavior index is multiplied by a corresponding pre-generated weight, and the scores of the current driving behavior indexes after the weight calculation are added to obtain a first calculation result of the driver.
In the embodiment, the driving behavior of the driver is scored based on the current driving data to obtain a first score, wherein the score is a percentile system.
In this embodiment, since each driver has different gender, age, driving habits, traveling habits, and the like, and since different types of vehicles have different performances, it is not comprehensive enough to analyze the driving behavior only according to the current driving data of the vehicle, and therefore, the first score needs to be adjusted based on the personnel tag and the vehicle information, and the driving behavior of the driver can be analyzed more pertinently and comprehensively.
As an example, the adjustment ratio for adjusting the first score based on the personnel tag and the vehicle information may be an adjustment ratio set based on historical experience effect data and a manual adjustment, and is not limited specifically.
As an example, based on the age label, different adjustment ratios are defined for young, middle-aged, and old people, respectively; respectively defining different adjustment proportions on sunny days, foggy days, rainy days and snowy days based on the weather condition of the current driving cycle; different adjustment ratios are respectively defined based on the production date and the model of the vehicle.
As an example, if the first score obtained above is 78 points, the second score is 86 points after adjustment based on the adjustment ratio.
In this embodiment, the driving behavior of the driver is classified based on a correspondence between a preset second score and a driving behavior type.
As an example, the preset second score may correspond to the driving behavior type by 60 to 80 points for the safe driving behavior, 80 to 100 points for the economic driving behavior, 0 to 60 points for the loss driving behavior, and the like, which is not limited specifically.
As an example, if the obtained second score is 86 points, the type to which the driving behavior of the driver belongs is economy, and if the obtained second score is 56 points, the type to which the driving behavior of the driver belongs is loss.
As an example, the type of driving behavior is obtained while classifying drivers as novice drivers, skilled drivers, and old drivers, providing a human interactive experience that makes it easier for drivers to accept reminders and notifications.
In this embodiment, the step of calculating the target score based on an entropy weight method to obtain the weight of each driving behavior index includes:
step C1: carrying out standardization processing on the target score to obtain a standardized target score;
step C2: calculating the standardized target score to obtain the information entropy of each current driving behavior index;
step C3: and obtaining the weight corresponding to each current driving behavior index based on the information entropy and the target score.
In this embodiment, the score of each current driving behavior index is determined based on the corresponding relationship between the preset driving behavior index and the target score, the score of each current driving behavior index conforms to the form of normal distribution, abnormal values far away from the distribution are cleaned based on the expected value, the influence of some abnormal driving behaviors on the driving behavior score can be removed, the expected value of the index can be automatically updated by supplementing historical data and manually modified, and the method is not limited specifically.
In this embodiment, after the current driving behavior index scores are cleaned, the cleaned scores are subjected to standardization processing to obtain a standardization result;
as an example, the normalized target score is calculated to obtain an information entropy of each current driving behavior index; and obtaining the weight corresponding to each current driving behavior index based on the information entropy and the target score.
As an example, the weight may be calculated and manually set, and is not limited in particular.
Step S30: acquiring target driving data in a preset time period before and after the high energy consumption event occurs, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on monitoring energy consumption data in the current driving data by a vehicle-mounted monitoring terminal;
in this embodiment, after the correlation between the driving behavior and the economic energy consumption is obtained based on the evaluation of the driving behavior of the driver, the correlation between the driving data of the current vehicle and the energy consumption needs to be evaluated, and for convenience of evaluation, only the driving data in the high energy consumption event occurrence period with higher correlation is analyzed, where the high energy consumption event is determined based on the monitoring of the energy consumption data in the current driving data by the vehicle-mounted monitoring terminal, and if it is monitored that the energy consumption data is higher than the preset energy consumption threshold, the high energy consumption event is determined to occur.
The step of determining the correlation of the high energy consumption event with the driving data during the high energy consumption event occurrence period comprises:
step C1: and analyzing the variation trend of each driving data in the high-energy-consumption event occurrence period on the same time axis to obtain the correlation between the high-energy-consumption event and the target driving data.
As an example, as shown in fig. 2, an on-board monitoring terminal monitors CAN data, monitors average energy consumption of a current engine for hundreds of kilometers, records a high energy consumption event when the energy consumption is higher than a preset energy consumption threshold, locally stores the CAN data within an occurrence period of the high energy consumption event, compresses and stores the data in a compression format, and uploads the data to an intelligent networking platform through SFTP (secure File Transfer Protocol), the intelligent networking platform uploads the compressed data to an enterprise data lake of a vehicle operation enterprise platform, the data lake stores the compressed data in an original format, and data granularity (refinement and integration degree of data stored in the data lake) is subject to definition in DBC (database management software) and performs deduplication processing on continuous events at the same time.
As an example, the intelligent network interconnection platform manages the DBC of the vehicle-mounted monitoring terminal and dynamically analyzes the data, and the DBC is compressed and then uploaded to the big data center, and the big data center analyzes and stores the data in real time, so as to locate the high energy consumption event and realize dynamic data uploading.
As an example, the high energy consumption event occurrence period may be within 30 seconds before and after the occurrence of the high energy consumption event, within 40 seconds before and after the occurrence of the high energy consumption event, and the like, and is not limited in particular.
As an example, obtaining each driving data in the occurrence period of the high energy consumption event from a data lake, placing the driving data on the same time axis, observing the variation trend of the driving data, and analyzing the variation trend to obtain the correlation between the high energy consumption event and the target driving data.
As an example, the correlation may be a degree of correlation, whether the correlation is related, and the like, and is not limited in detail.
Step S40: and based on the type and the correlation, proposing energy consumption optimization suggestions to the driver and the vehicle.
In the embodiment, energy consumption optimization suggestions are provided for the driver and the vehicle development based on the analysis result of the driving behavior of the driver and the correlation between the high energy consumption event and the driving data in the high energy consumption event occurrence period.
As an example, the energy consumption optimization suggestion may be, without limitation, to normalize the driving behavior of the driver and improve the vehicle performance and driving comfort of the vehicle.
As an example, when the driver finishes using the vehicle, the mobile terminal inputs the driving finishing result, and the mobile terminal feeds back the energy consumption optimization suggestion made for the driver to the driver, and the suggestion and the prompt are used for the driver, so that the energy consumption can be optimized on the aspect of the driver.
As an example, after the driver finishes using the vehicle, the vehicle stores the driving data during driving in the enterprise data lake, so that the research and development personnel can improve the performance of the vehicle at the research and development side.
In the embodiment, the driving behavior of the driver is evaluated based on the driving behavior of the driver and the state data of the vehicle and the personnel label of the driver, and the energy consumption optimization suggestion is provided for the driver and the vehicle by combining the correlation between the high energy consumption event and the driving data in the occurrence period of the high energy consumption event, so that the energy consumption optimization can be carried out from multiple angles, and the accurate and comprehensive energy consumption optimization suggestion is provided.
Further, based on the first embodiment and the second embodiment in the present application, another embodiment of the present application is provided, in which after the step of determining the correlation between the high energy consumption event and the target driving data, the method further includes:
step E1: acquiring original driving data of a current vehicle, and arranging the original driving data according to a time sequence of occurrence to obtain a time sequence database;
step E2: and performing frequency analysis on the time sequence data related to the high energy consumption events in the time sequence database, determining the data characteristics of the time sequence data related to the high energy consumption events, and generating an energy consumption experience database for use in vehicle research and development.
As an example, the CAN bus collects the driving data generated during the driving process of the vehicle in real time and stores the driving data locally, so that the CAN frame data is the original driving data capable of truly reflecting the driving state of the vehicle.
As an example, raw CAN frame data is collected, and due to reasons such as network, the vehicle driving data uploaded by the CAN bus has timeliness, so that the CAN frame data needs to be arranged in the occurrence time sequence to obtain a time sequence database.
As an example, the time sequence data related to the high energy consumption events in the time sequence database is subjected to frequency analysis to obtain the correlation between the time sequence data related to the high energy consumption events and the high energy consumption events, the data characteristics of the time sequence data related to the high energy consumption events are determined, and the energy consumption experience base is generated based on the data characteristics
For use in vehicle development.
In the embodiment, the energy consumption experience base feeds data generated when a high energy consumption event occurs back to vehicle research and development personnel, so that the research and development of the vehicle are closer to the actual situation, the research and development efficiency is improved, and the research and development cost is reduced.
Referring to fig. 3, fig. 3 is a schematic device structure diagram of a hardware operating environment according to an embodiment of the present application.
As shown in fig. 3, the vehicle energy consumption optimizing apparatus may include: a processor 1001, a memory 1005, and a communication bus 1002. The communication bus 1002 is used to enable connection communication between the processor 1001 and the memory 1005.
Optionally, the vehicle energy consumption optimization device may further include a user interface, a network interface, a human face camera, an RF (Radio Frequency) circuit, a sensor, a WiFi module, and the like. The user interface may comprise a Display screen (Display), an input sub-module such as a Keyboard (Keyboard), and the optional user interface may also comprise a standard wired interface, a wireless interface. The network interface may include a standard wired interface, a wireless interface (e.g., WI-FI interface).
Those skilled in the art will appreciate that the configuration of the vehicle energy consumption optimization device shown in fig. 3 does not constitute a limitation of the vehicle energy consumption optimization device, and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 3, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, and a vehicle energy consumption optimization program. The operating system is a program that manages and controls the hardware and software resources of the vehicle energy consumption optimizing device, supporting the operation of the vehicle energy consumption optimizing program as well as other software and/or programs. The network communication module is used for communication among the components in the memory 1005 and with other hardware and software in the vehicle energy consumption optimization system.
In the vehicle energy consumption optimization apparatus shown in fig. 3, the processor 1001 is configured to execute a vehicle energy consumption optimization program stored in the memory 1005 to implement the steps of the vehicle energy consumption optimization method described in any one of the above.
The specific implementation of the vehicle energy consumption optimization device of the present application is substantially the same as that of each embodiment of the vehicle energy consumption optimization method, and is not described herein again.
The present application further provides a vehicle energy consumption optimization device, the device includes:
the system comprises a first generation module, a second generation module and a third generation module, wherein the first generation module is used for generating a personnel label based on personal information of a driver and historical driving data thereof;
the first determining module is used for analyzing the driving behavior of the driver based on the personnel tag and the current driving data to obtain the type of the current driving behavior of the driver;
the second determining module is used for acquiring target driving data in a preset time period before and after the high-energy-consumption event occurs, and determining the correlation between the high-energy-consumption event and the target driving data, wherein the high-energy-consumption event is determined based on monitoring energy consumption data in the current driving data by the vehicle-mounted monitoring terminal;
and the proposing module is used for proposing energy consumption optimization suggestions to the driver and the vehicle based on the type and the correlation.
Optionally, in a possible implementation manner of the present application, the first determining module includes:
the input unit is used for inputting the current driving data into a preset driving behavior analysis model;
and the processing unit is used for analyzing and processing the current driving data based on the preset driving behavior analysis model to obtain the type of the driving behavior of the driver, wherein the preset driving behavior analysis model is obtained by performing iterative training on a preset model to be trained based on training data with type labels.
Optionally, in a possible implementation manner of the present application, the processing unit is configured to: calculating the current driving data to obtain at least one current driving behavior index; the target score of each current driving behavior index is determined based on the corresponding relation between the preset driving behavior index and the target score; the method is also used for calculating the target score based on an entropy weight method to obtain the weight of each driving behavior index; the driving behavior index calculating unit is further used for calculating the target score of each current driving behavior index based on the weight of each driving behavior index to obtain a first calculation result; the system is also used for determining an adjustment coefficient of the first calculation result based on a preset personnel tag and the current driving data; the adjusting module is further used for adjusting the first calculation result based on the adjusting coefficient to obtain a second calculation result; and the method is also used for determining the type of the current driving behavior of the driver based on the corresponding relation between the preset second score and the driving behavior type.
And/or the processing unit is further configured to perform normalization processing on the target score to obtain a normalized target score; the driving behavior index calculation module is also used for calculating the standardized target score to obtain the information entropy of each current driving behavior index; and the weight corresponding to each current driving behavior index is obtained based on the information entropy and the target score.
Optionally, in a possible implementation manner of the present application, the adjusting the second determining module includes:
and the third determining module is used for analyzing the variation trend of each driving data in the high-energy-consumption event occurrence period on the same time axis to obtain the correlation between the high-energy-consumption event and the target driving data.
Optionally, in a possible implementation manner of the present application, the apparatus further includes:
the acquisition module is used for acquiring the original driving data of the current vehicle;
the arrangement module is used for arranging the original driving data according to the occurrence time sequence to obtain a time sequence database;
the analysis module is used for carrying out frequency analysis on the time sequence data related to the high energy consumption events in the time sequence database;
the second determination module is used for determining the data characteristics of the time sequence data related to the high-energy-consumption event;
and the second generation module is used for generating an energy consumption experience library for the vehicle to use in research and development.
The specific implementation of the vehicle energy consumption optimization method of the present application is substantially the same as that of each embodiment of the vehicle energy consumption optimization method described above, and is not described herein again.
The embodiment of the application provides a storage medium, and the storage medium stores one or more programs, which can be executed by one or more processors for implementing the steps of the vehicle energy consumption optimization method described in any one of the above.
The specific implementation of the storage medium of the present application is substantially the same as that of each embodiment of the vehicle energy consumption optimization method, and is not described herein again.
The embodiment of the application provides a vehicle energy consumption optimizing system, the system includes: vehicle monitoring terminal and vehicle energy consumption optimization device according to any of the preceding claims.
The specific implementation of the vehicle energy consumption optimization system of the present application is substantially the same as that of each embodiment of the vehicle energy consumption optimization method, and is not described herein again.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or system in which the element is included.
The above-mentioned serial numbers of the embodiments of the present invention are only for description, and do not represent the advantages and disadvantages of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present invention or portions thereof contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) as described above and includes several instructions for enabling a terminal device (which may be a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A vehicle energy consumption optimization method, characterized in that it comprises the steps of:
generating a personnel tag based on personal information of a driver and historical driving data thereof;
analyzing the driving behavior of the driver based on the personnel label and the current driving data to obtain the type of the current driving behavior of the driver;
acquiring target driving data in a preset time period before and after the high energy consumption event occurs, and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on monitoring energy consumption data in the current driving data by a vehicle-mounted monitoring terminal;
and based on the type and the correlation, proposing energy consumption optimization suggestions to the driver and the vehicle.
2. The vehicle energy consumption optimization method according to claim 1, wherein the step of analyzing the driving behavior of the driver based on the personnel tags and the current driving data to obtain the type of the current driving behavior of the driver comprises:
inputting the current driving data into a preset driving behavior analysis model;
and analyzing and processing the current driving data based on the preset driving behavior analysis model to obtain the type of the driving behavior of the driver, wherein the preset driving behavior analysis model is obtained by iteratively training a preset model to be trained based on training data with type labels.
3. The vehicle energy consumption optimization method according to claim 2, wherein the step of analyzing and processing the current driving data based on the preset driving behavior analysis model to obtain the type of the driving behavior of the driver comprises:
calculating the current driving data to obtain at least one current driving behavior index;
determining a target score of each current driving behavior index based on the corresponding relation between the preset driving behavior index and the target score;
calculating the target score based on an entropy weight method to obtain the weight of each driving behavior index;
calculating the target score of each current driving behavior index based on the weight of each driving behavior index to obtain a first calculation result;
determining an adjustment coefficient for the first calculation result based on a preset personnel tag and the current driving data;
adjusting the first calculation result based on the adjustment coefficient to obtain a second calculation result;
and determining the type of the current driving behavior of the driver based on the corresponding relation between the preset second calculation result and the driving behavior type.
4. The vehicle energy consumption optimization method according to claim 3, wherein the step of calculating the target score based on an entropy weight method to obtain the weight of each driving behavior index further comprises:
carrying out standardization processing on the target score to obtain a standardized target score;
calculating the standardized target score to obtain the information entropy of each current driving behavior index;
and obtaining the weight corresponding to each current driving behavior index based on the information entropy and the target score.
5. The vehicle energy consumption optimization method of claim 1, wherein the step of determining the correlation of the high energy consumption event to the target driving data comprises:
and analyzing the variation trend of each driving data in the high-energy-consumption event occurrence period on the same time axis to obtain the correlation between the high-energy-consumption event and the target driving data.
6. The vehicle energy consumption optimization method of claim 1, wherein after the step of determining the correlation of the high energy consumption event to the target driving data, the method further comprises:
acquiring original driving data of a current vehicle, and arranging the original driving data according to the occurrence time sequence to obtain a time sequence database;
and performing frequency analysis on the time sequence data related to the high energy consumption events in the time sequence database, determining the data characteristics of the time sequence data related to the high energy consumption events, and generating an energy consumption experience database for use in vehicle research and development.
7. An apparatus for optimizing energy consumption of a vehicle, the apparatus comprising:
the generation module is used for generating a personnel label based on the personal information of the driver and the historical driving data thereof;
the first determination module is used for analyzing the driving behavior of the driver based on the personnel label and the current driving data to obtain the type of the current driving behavior of the driver;
the second determining module is used for acquiring target driving data in a preset time period before and after a high energy consumption event occurs and determining the correlation between the high energy consumption event and the target driving data, wherein the high energy consumption event is determined based on monitoring energy consumption data in the current driving data by the vehicle-mounted monitoring terminal;
and the proposing module is used for proposing energy consumption optimization suggestions to the driver and the vehicle based on the type and the correlation.
8. An apparatus for optimizing energy consumption of a vehicle, the apparatus comprising: memory, a processor and a vehicle energy consumption optimization program stored on the memory and executable on the processor, the vehicle energy consumption optimization program being configured to implement the steps of the vehicle energy consumption optimization method according to any one of claims 1 to 6.
9. A storage medium, characterized in that the storage medium has stored thereon a vehicle energy consumption optimization program which, when executed by a processor, implements the steps of the vehicle energy consumption optimization method of any one of claims 1 to 6.
10. The vehicle energy consumption optimization system of claim 1, wherein the system comprises: an on-board monitoring terminal and a vehicle energy consumption optimization device according to claim 7.
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| CN116992998A (en) * | 2023-06-03 | 2023-11-03 | 隆瑞三优新能源汽车科技有限公司 | New energy bus charging condition prediction method and device, electronic equipment and medium |
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| CN116992998A (en) * | 2023-06-03 | 2023-11-03 | 隆瑞三优新能源汽车科技有限公司 | New energy bus charging condition prediction method and device, electronic equipment and medium |
| CN116992998B (en) * | 2023-06-03 | 2024-03-26 | 隆瑞三优新能源汽车科技有限公司 | New energy bus charging condition prediction method and device, electronic equipment and medium |
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